How to Use the Shopline MCP in CrewAI

Q: If I need to audit Shopline collections, how does CrewAI manage the process?

You assign an agent the task of calling listcollections first. A second agent then takes that list and iterates through it, calling other tools like listproducts for every single collection. This structured handoff makes complex data mapping reliable.

Q: What is the best way to handle Shopline user data with CrewAI?

Define a 'User Management' role. Give this agent access to listcustomers and potentially getorderdetails. This allows the agents to collaborate on tasks like verifying customer activity or summarizing account health.

Run specialized Shopline operations with CrewAI multi-agent teams.

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Connect Shopline MCP to CrewAI

Create your Vinkius account to connect Shopline to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Shopline with CrewAI

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Coordinate Product Auditing Teams

You can set up a team where one agent calls `list_products` to pull the full inventory list. A second, specialized agent then takes that data and runs it through `get_product_details` for validation checks. This separates research from analysis. The roles are clear: Agent Alpha gathers all items; Agent Beta analyzes their specific attributes (like stock levels or descriptions). The shared memory makes sure the entire team is working off the exact same, current dataset.

Model Order Fulfillment Workflows

Build a process where one agent researches pending orders using `list_orders`. A second agent then checks each order's specific details via `get_order_details` to determine fulfillment status. This simulates a real-world logistics check. The crew model is perfect here because you assign roles: the 'Order Researcher' and the 'Fulfillment Checker.' They pass information back and forth until the entire operation—like flagging all delayed orders—is complete.

Analyze Store Structure and Users

Need to understand the scope? Assign one agent the role of 'Store Analyst' who uses `get_shop_info` for basic parameters. Another agent, the 'Client Manager,' runs `list_customers`. The team then reviews both datasets together. This specialized collaboration allows you to build autonomous operations—like generating a summary report that includes store details and customer counts—without needing manual intervention.

Setup guide

Set up Shopline MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Shopline tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Shopline Analyst",
    goal="Access and analyze Shopline data via MCP.",
    backstory="Expert analyst with direct Shopline access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Shopline transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Shopline Analyst",
        goal="Access and analyze Shopline data via MCP.",
        backstory="Expert analyst with direct Shopline access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Shopline transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Shopline. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Shopline MCP in CrewAI

You define distinct roles for your agents. One agent might be assigned the task of listing products via `list_products`, while a second is assigned to check customer profiles using `get_product_details`. They pass results back and forth, building a complete operational output.

You assign an agent the task of calling `list_collections` first. A second agent then takes that list and iterates through it, calling other tools like `list_products` for every single collection. This structured handoff makes complex data mapping reliable.

Define a 'User Management' role. Give this agent access to `list_customers` and potentially `get_order_details`. This allows the agents to collaborate on tasks like verifying customer activity or summarizing account health.

Absolutely. You can set up a team where one agent pulls all orders via `list_orders`. The second agent then uses `get_order_details` on each record to check the specific status, allowing you to flag bottlenecks autonomously.

It provides access to all key product lifecycle data: listing all products (`list_products`), getting details on single items (`get_product_details`), and seeing which collections they belong to.

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